Method and system for the derivation of human gait characteristics and detecting falls passively from floor vibrations

ABSTRACT

The gait monitor system and method provides various basic gait parameters including step count, cadence, and step duration, in addition to its ability to distinguish between normal, limping and shuffling gait modes, as well as determine falls. Moreover, this gait monitor may be provided with additional sensors, e.g. beam break at the beginning and end of a corridor to estimate average walking velocity (with the distance between the beams known or determined); this enables the calculation of additional gait characteristics such as average step length and average stride length. These parameters can additionally be used to detect various gait anomalies and other diagnostic information.

RELATED APPLICATIONS

This application is a national stage filing of International ApplicationNo. PCT/US2004/009098 which claims benefit under 35 U.S.C Section 119(e)of the earlier filing date of U.S. Provisional Application Ser. No.60/460,237, filed on Apr. 3, 2003, entitled “Method and System for theDerivation of Basic Human Gait Characteristics and Detecting FallsPassively from Floor Vibrations,” of which the entire disclosures arehereby incorporated by reference herein.

BACKGROUND OF THE INVENTION

Gait analysis has many applications ranging from rehabilitation tosports medicine, orthopedics and studying the effectiveness ofprosthetics to improve their design. See Joseph C, Andrew G., “GaitAnalysis in the Amputee: Has it Helped the Amputee or Contributed to theDevelopment of Improved Prosthetic Components?” Gait Posture (1996) 4,258-68, of which is hereby incorporated by reference herein in itsentirety. Long-term in-home gait monitoring not only can provide ameasure of a person's functional ability, but it also can provide ameasure of activity levels and may therefore help ‘evaluate’ a person'shealth over a long period of time. Passive in-home gait monitoring canbe useful for assessing healing/deterioration following therapeuticinterventions including surgeries, drug or physical therapy. Moreover,the ability to identify negative trends of subtle changes in a person'sgait can contribute to detection of health problems at early stages ofdisease onset. Research also indicates that certain gait characteristicscan be used as a biometric for identification purposes. See Little J,Boyd J., “Recognizing People by Their Gait: the Shape of Motion,”Videre, Winter 1998, of which is hereby incorporated by reference hereinin its entirety. See Orr R, Abowd G., “The Smart Floor: A Mechanism forNatural User Identification and Tracking Conference on Human Factors inComputing Systems,” April 2000, of which is hereby incorporated byreference herein in its entirety.

On the other hand, falls are a major cause of morbidity in the elderly.See François P, Helene C, Réjean H, David W., “Gait in the Elderly,”Gait and Posture (1997) 5(2), 128-135, of which is hereby incorporatedby reference herein in its entirety. They are responsible for 70 percentof accidental deaths in persons 75 years of age and older. The elderly,who represent 12 percent of the population, account for 75 percent ofdeaths from falls. See George F., “Falls in the Elderly,” AmericanFamily Physician, April 2000, of which is hereby incorporated byreference herein in its entirety. The considerable cost involved in thetreatment and Hospitalization of fall injuries and even death due tofalls could be greatly reduced if falls could be predicted and avoidedthrough appropriate intervention. An in-home gait-monitoring tool withthe ability to distinguish between normal walking and abnormal gait mayhelp predict a propensity for injurious falls. See Stalenhoef P A,Diederiks J P, Knottnerus J A, Kester A D, Crebolder H F., “A Risk Modelfor the Prediction of Recurrent Falls in Conumunity-Dwelling Elderly: aProspective Cohort Study,” J Clin Epidemiol November 2000;55(11):1088-94, of which is hereby incorporated by reference herein inits entirety. See Azizah Mbourou G, Lajoie Y, Teasdale N., “Step LengthVariability at Gait Initiation in Elderly Fallers and Non-Fallers, andYoung Adults,” Gerontology. January-February 2003; 49(1):21-6, of whichis hereby incorporated by reference herein in its entirety.

Human gait analysis entails numerous parameters that can be classifiedinto spatio-temporal, kinematic and kinetic characteristics.Spatio-temporal parameters include average walking velocity, stridelength, step length, step time, cadence, stance phase time, swing phasetime, single support (when only one foot is in contact with the floor),double support (when both feet are in contact with the floor), andstride width. Kinematic parameters study the angles between the ankle,hip and knee joints. Finally, kinetic analysis examines moments, energyand power at these joints. See Craik R, Oatis C., “Gait Analysis Theoryand Application,” Mosby 1995, of which is hereby incorporated byreference herein in its entirety.

Most gait analysis laboratories use visual means for gait analysis wherekinematic (See Dockstader S, Tekalp A., “A Kinematic Model for HumanMotion and Gait Analysis,” Proc. of the Workshop on Statistical Methodsin Video Processing (ECCV), Copenhagen, Denmark, 1-2 June 2002, pp.49-54, of which is hereby incorporated by reference herein in itsentirety) and biomechanical models (See Simon J, Metaxiotis D, Siebel A,Bock H, Döderlein L., “A Multi-Segmented Foot Model,” 6th Annual Gaitand Clinical Movement Analysis Meeting, Shriners Hospitals for Children,Northern California, of which is hereby incorporated by reference hereinin its entirety) are built from visually acquired gait data. A review ofthe various visual human motion and gait analysis techniques can befound in the Aggarwal J, and Cai Q. article (See Aggarwal J, Cai Q.,“Human Motion Analysis: A Review,” Proceedings, IEEE Nonrigid andArticulated Motion Workshop, June 1997, of which is hereby incorporatedby reference herein in its entirety.) and Gavrila D. article (SeeGavrila D., “The Visual Analysis of Human Movement: A Survey,” ComputerVision and Image Understanding, 73(1): 82-98, January 1999, of which ishereby incorporated by reference herein in its entirety.) An excellentoverview of in-the-lab gait analysis tools, methods and applications inrehabilitation can be found in the DeLisa J. article (See DeLisa J.,“Gait Analysis in the Science of Rehabilitation,” VARD Monograph 002,1998, of which is hereby incorporated by reference herein in itsentirety.). Gait lab equipment and analysis techniques yield excellentand detailed gait characteristics and enable clinicians to prescribe anappropriate intervention. However, the equipment required for afunctional gait laboratory is extremely expensive, in the range of tensof thousands to a few hundred thousand dollars in equipment andsoftware. Additionally, the computational power required for the imagebased analysis make longitudinal in-home gait monitoring using thesetechnologies impractical. Moreover, people are normally referred to gaitlabs for full gait analysis only after the changes in their gait havebecome obvious. Gait Laboratories also use pressure measurement systemssuch as force plates for gait analysis. Force plate data can revealimportant information such as a quantitative evaluation of the effect ofTotal Knee Arthoplasty (TKA) in patients with osteoarthritis. See OtsukiT, Nawata K, Okuno M., “Quantitative Evaluation of Gait Pattern inPatients With Osteoarthritis of the Knee Before and After Total KneeArthoplasty. Gait Analysis Using a Pressure Measuring System,” Journalof Orthopaedic Science, 4(2): 99-105, 1999, of which is herebyincorporated by reference herein in its entirety. The pressure systemmeasured Stance phase timing and forces. However, research at the OhioState University demonstrated that force plate size influenced validgait data acquisition (See Oggero E, Pagnacco G, Morr D R, Berme N.,“How Force Plate Size Influences the Probability of Valid Gait DataAcquisition,” Biomedical Sciences Instrumentation, 35:3-8 1999, of whichis hereby incorporated by reference herein in its entirety) and thatsome subjects must alter their gait for valid gait data acquisition (SeeOggero E, Pagnacco G, Morr D R, Simon S R, Berme N., “Collecting ValidData From Force Plates: How Many Subjects Must Alter Their Gait?” NorthAmerican Congress on Biomechanics 2000, of which is hereby incorporatedby reference herein in its entirety.).

Current outside the lab gait analysis techniques broadly fall underthree categories depending upon the type of device used, wearabledevices, walk on devices and visual gait analysis tools and techniques.Wearable devices include actigraphs and devices such as that describedin the gait activity monitor to Smith et al. (See U.S. Pat. No.5,485,402 to Smith et al., entitled “Gait Activity Monitor,” of which ishereby incorporated by reference herein in its entirety.) These devicesmeasure acceleration resulting from movement of the body or limb thatmay not necessarily correspond to walking. Moreover, accelerometers donot provide enough information to enable the evaluation of the actualcharacteristics of the gait. The gait activity monitor described in Weiret al. (See U.S. Pat. No. 5,831,937 to Weir et al., entitled “PortableRanging System for Analyzing Gait;” of which is hereby incorporated byreference herein in its entirety.) is worn on the ankle with built-inoptical communication. Another variation on this type of devices isdescribed in Portable Ranging System, where a combination of ultrasoundand infrared is used to periodically determine the distance between aperson and a base station (See U.S. Pat. No. 5,623,944 to Nashner,entitled “Method for Characterizing Gait,” of which is herebyincorporated by reference herein in its entirety; this device is mainlyused to measure velocity). Walk-on gait analysis devices includetreadmills (See U.S. Pat. No. 5,952,585 to Trantzas et al., entitled“Portable Pressure Sensing Apparatus for Measuring Dynamic Gait Analysisand Method of Manufacture;” of which is hereby incorporated by referenceherein in its entirety), mats (See U.S. Pat. No. 6,360,597 B1 toHubbard, Jr., entitled “In-Shoe Remote Telemetry Gait Analysis System,of which is hereby incorporated by reference herein in its entirety),special shoes (See Classification of Gait Abnormalities:http://guardian.curtin.edu.au/cga/faq/classification.html, of which ishereby incorporated by reference herein in its entirety.), and speciallydesigned floors (See Orr R, Abowd G., “The Smart Floor: A Mechanism forNatural User Identification and Tracking Conference on Human Factors inComputing Systems,” April 2000, of which is hereby incorporated byreference herein in its entirety.). The treadmill described in ‘Methodfor characterizing gait’ (See Gavrila D., “The Visual Analysis of HumanMovement: A Survey,” Computer Vision and Image Understanding, 73(1):82-98, January 1999, of which is hereby incorporated by reference hereinin its entirety.) has transducers mounted below the movable surface thatcan measures force from each foot individually can differentiate betweenwalking and running. Arrays of pressure sensors are placed under aflexible mat sheet are described in (See U.S. Pat. No. 5,952,585, ofwhich is hereby incorporated by reference herein in its entirety.) tomeasure force and other gait parameters. Another approach (See U.S. Pat.No. 6,360,597, of which is hereby incorporated by reference herein inits entirety), describes an in-shoe pressure sensing system with anexternal telemetry transmitter. The pressure sensor data is transmittedto a remote computer for analysis. Another potential method for gaitanalysis is to have a ‘smart floor’ comprising force plate tiles orembedding load cells under individual tiles (See Orr R, Abowd G., “TheSmart Floor: A Mechanism for Natural User Identification and TrackingConference on Human Factors in Computing Systems,” April 2000, of whichis hereby incorporated by reference herein in its entirety.) to measurecharacteristics of footsteps; this approach is expensive.

BRIEF SUMMARY OF INVENTION

The present invention passive gait monitor system and method describedin this disclosure are based on detecting vibrations generated by aperson or animal walking on the floor. An embodiment is implementedusing an ultra sensitive optic-fiber sensor that is capable of tens offeet away from the sensor on both carpeted and uncarpeted wooden andconcrete floors. However, the methods described can be applied to othervibration, acceleration, and/or deflection sensors and sensingtechnologies, including but not limited to piezoelectric,electromechanical, optic, laser, and fiber optic sensors. The vibration,acceleration, and/or deflection sensor can be fixed in a corridor or awalkway within the home environment or any desired environment, forshort term and extended term monitoring of changes in gait mode anddrifts in cadence that may indicate a heightened fall risk, as well asactual fall. Since it can be deployed in natural settings and the userdoes not need to wear any devices, walk on special surfaces or beobserved by cameras, this gait monitor is completely passive andunobtrusive; hence, the “white coat” stress effect associated with aclinical test could be reduced or even totally eliminated.

The device and method can detect falls and can be augmented toautomatically initiate an alert call to designated care providers oremergency services in the event of a detected fall episode followed by aperiod of inactivity.

Other applications include unobtrusive gait analysis in clinicalsettings. One can envision the deployment of such a monitor in acorridor within the clinic, where a person's gait is preliminarilyevaluated as he/she enters the clinic and that the analysis report isavailable to the clinician by the time the patient walks into theexamination room. Longitudinal data, together with more elaborateanalysis techniques lead to a fall prediction model. Other data mayinclude, for example, pattern recognition or identificationdetermination of the subject (human or animal) being monitored. Anembodiment of the sensing unit of the passive gait monitor is physicallysmall, low-cost, and designed to transmit acquired data via hardwired orwireless means. Thus, this embodiment of this passive gait monitor maybe ideally suited to monitoring the ‘natural gait’ of a person duringregular activity, in the home or in the clinic, to provide basic butessential gait characteristics.

Results obtained from a prototype design and detection algorithmsapplied to recorded raw sensor data demonstrate that this deviceprovides a wide range of different applications, including biometrics.

An aspect of an embodiment of the present invention provides a gaitmonitoring for monitoring gait characteristics of a subject. The systemcomprising: a sensor module that detects floor acceleration, vibration,and/or deflection to provide acceleration, vibration, and/or deflectionsignal; and a processor module that analyzes the acceleration,vibration, and/or deflection signal for determining gaitcharacteristics.

An aspect of an embodiment of the present invention provides a methodfor monitoring gait characteristics of a subject. The method comprising:detecting floor acceleration, vibration, and/or deflection to provideacceleration, vibration, and/or deflection signal; and analyzing theacceleration, vibration, and/or deflection signal for determining gaitcharacteristics.

An aspect of an embodiment of the present invention provides a computerprogram product comprising computer usable medium having computer logicfor enabling at lease one processor in a computer system or the like tomonitor gait characteristics of a subject. The computer logiccomprising: detecting floor acceleration, vibration, and/or deflectionto provide acceleration, vibration, and/or deflection signal; andanalyzing the acceleration, vibration, and/or deflection signal fordetermining gait characteristics.

These and other objects, along with advantages and features of theinvention disclosed herein, will be made more apparent from thedescription, drawings, and claims that follow.

BRIEF SUMMARY OF THE DRAWINGS

The foregoing and other objects, features and advantages of the presentinvention, as well as the invention itself, will be more fullyunderstood from the following description of potential embodiments, whenread together with the accompanying drawings in which:

FIG. 1 is a schematic block diagram of the subject and gait monitoringsystem.

FIG. 2 is a schematic block diagram of the gait monitoring system.

FIGS. 3(A)-(B) are schematic plan and elevation views, respectively, ofan embodiment of the present invention acceleration, vibration, and/ordeflection module.

FIGS. 3(C)-(D) show a graphical representation of the “raw” and postprocessing signals produced in the embodiment of FIGS. 3(A)-(B), whereinthe steps are as captured in FIG. 3(D) while the raw signal shows therich harmonic content as captured in FIG. 3(C).

FIG. 4 is a schematic block diagram of an example of the gait monitoringsystem.

FIG. 5 shows a graphical representation of an example of the resultsobtained applying an exemplary circuit model to the data obtained fromthe floor vibration sensor for a normal walk.

FIG. 6 shows a graphical representation of an example of the resultsobtained applying an exemplary circuit model to the data obtained fromthe floor vibration sensor for a limp.

FIG. 7 shows a graphical representation of an example of the resultsobtained applying an exemplary circuit model to the data obtained fromthe floor vibration sensor for a shuffle.

FIG. 8 shows a graphical representation of an example of the resultsobtained applying an exemplary circuit model to the data obtained fromthe floor vibration sensor for a fall.

FIG. 9 shows the graphically shows an example demonstrating the falldetector's reduced sensitivity to falling objects.

DETAILED DESCRIPTION OF THE INVENTION

The floor vibration sensor employed in any embodiment of the gaitmonitor can be any variety of sensor modalities including, but notlimited to: magnetic coil induction; laser light reflection; changes inPlasmon surface resonance; RF; changes in light due to luminescence;Doppler radar; and/or any sensor technology that transduces the minutedeflections of the floor induced by gait or falling or dropping ofobjects.

The slightest vibrations imparted on the active sensor element eitherdirectly or through but not limited to mechanical, acoustic or opticalmeans yield a signal that varies in an analog of the floor vibration ordisplacement. The sensor element may be attached to any surfaceincluding but not limited to the floor itself, the baseboard of a wall,a wall, the ceiling, and below the floor. The sensor element may befreestanding, using displacement against moment of inertia of the systemor where displacement is determined by difference between the modulatingsurface and another plane including but not limited to the baseboard ofa wall, a wall, the ceiling, and below the floor. Various supportelectronics may be used to provide detection, amplification andfiltering of the transduced signals.

Moreover, it should be appreciated that the vibration sensor employed inany embodiment of the gait monitor may be various optic sensors, forexample such fiber optic sensors as employed in U.S. Pat. No. 6,687,424B1 to Gerdt et al., entitled “Sensing Pad Assembly Employing VariableCoupler Fiberoptic Sensor;” U.S. Pat. No. 6,463,187 B1 to Baruch et al.,entitled “Variable Coupler Fiberoptic Sensor and Sensing Apparatus Usingthe Sensor;” U.S. Patent Application Publication 2003/0199771 A1 toBaruch et al., entitled “Apparatus and Method for Measuring PulseTransit Time;” of which are hereby incorporated by reference herein intheir entirety. Other available fiber optic sensors may be employed aswell and can be any variety of sensor modalities.

Referring to FIG. 1, the sensing unit or module 32 is configured so thatit can measure floor 35 vibrations. The sensor module 32 can pick-upfloor vibrations generated by a person or subject 33 (or animate orinanimate object) walking tens of feet away (or any distance requiredfor setting or environment) from the sensor on both carpeted anduncarpeted wooden and concrete floors (or any given platform or base).As will be discussed in greater detail below, an embodiment of thepresent invention system processes the raw vibration signal of thesensor system and extracts features of significance and analyzes theextracted data to provide basic gait characteristics. In an embodiment,the processor module 40 analyzes or the like performs algorithms ormanipulation to differentiate between normal gait, limping and shufflingand measure step count and calculate cadence with good accuracy when thegait is normal. A rate of travel detector module 44 is provided to trackthe motion or travel span of the subject 33.

Referring to FIG. 2, FIG. 2 is a schematic block diagram of the gaitmonitoring system and related method for monitoring gait characteristicsof a subject (person, animal, animate or inanimate object). Anembodiment of the present invention system 31 includes a sensoracceleration, vibration, and/or deflection module 32 that detects flooracceleration, vibration, and/or deflection to provide the acceleration,vibration, and/or deflection signal. Examples include but are notlimited to magnetic coil induction; laser light reflection; changes inPlasmon surface resonance; RF; changes in light due to luminescence;doppler radar; and/or any sensor technology that transduces the minutedeflections of the floor induced by gait or falling or dropping ofobjects. A processor module 40 is provided that analyzes theacceleration, vibration, and/or deflection signal for determining gaitcharacteristics data obtained by the sensor module 32. The system 31and/or processor 40 is in communication with an output module 52 forreceiving the gait characteristics data. Examples of the output module52 include at least one of the following, but not limited thereto,display, alarm, memory storage, communication device, printer, buzzer,PDA, lap top computer, computer and/or light; or any available devicerequired for input/output. Examples of the communication device includeat least one of the following, but not limited thereto, modem, pager,network interface, Ethernet card, serial communications port, parallelcommunications port, telephone, and/or a PCMCIA slot and card; or anyother available device required for communication.

Still referring to FIG. 2, it should be appreciated that the system 31or only portions of the system or communication paths of the system 31(or with external devices) may be hardwired, wireless, or combinationthereof Examples of wireless communication include at least one of thefollowing, but not limited thereto, RF link, BLUE TOOTH, an infrared,cellular phone link, optical and/or electromagenetic; or any otheravailable wireless communication. Alternatively, the system 31 or onlyportions of the system or communication paths of the system 31 (or withexternal devices) may be hard wired, mechanical, optical, oroptical-mechanical, electromechanical communication. Some examples ofcommunication include at least one of the following, but not limitedthereto, integrated circuits, wire, cable, fiber optics, a phone line,twisted pair, and/or coaxial; or any mechanism capable of communicationtransmission.

Still referring to FIG. 2, an embodiment of the system may include atleast one rate-of-travel detector or module 44 to determine the rate oftravel of the subject. For example, the rate-of-travel detector may beany one of a system of beam breaks, floor switches, and door switches orany available systems capable to track the motion, movement, or travelspan of the subject. The rate-of-travel detector may operate in variousmodes including one of ultrasonic communication, IR communication, lasercommunication, ground radar communication, wide band radarcommunication, and/or doppler communication; or any other communicationpath or via necessary to effect the travel or motion detection.

Still referring to FIG. 2, an embodiment of the system may include atleast, one fall detector or module 36 that analyzes fall related dataand at least one step module 48 that analyzes step related data Themodule uses analog, digital or hybrid signal processing to reduce theraw signal for analysis and derivation of characteristics including butnot limited to physical forces that are currently known in gait such asheel toe impact, heel rotation, forces generated when the toes push offthe floor to get locomotion (e.g. gastricnemeous contraction), kneeflexure, hip rotation, and pelvis swivel, etc. These individual forcegenerators may be integrated into the amount of forward motion that isgenerated, versus the amount of motion that is spent lifting the body.The processor module 40 can provide numerous functions and operationsincluding, but not limited to: analyzing the acceleration, vibration,and/or deflection signal for determining gait characteristics dataobtained by the sensor module 32 and other components and modules of thesystem 31 or data or information received externally. The gaitcharacteristics of the subject includes at least one of, but not limitedthereto, step count, pace, normal condition, limp, shuffle, falls,average walking velocity, step length, and/or stride length; or anyother necessary parameter required or desired for a given application.

Still referring to FIG. 2, an embodiment of the system may include atleast one archival storage/memory 54. The archival storage/memory 54stores at least one of longitudinal analysis of gait characteristics,pattern recognition, and/or identification determination; or other dataas required or desired for given application. Further, the processormodule 40 or other secondary processors analyzes the gaitcharacteristics, pattern recognition, and/or identificationdetermination data In an embodiment the system further comprises asecond processor module (not shown). The second processor module may beconfigured to analyze the gait characteristics, pattern recognition,and/or identification determination data; or other data as required ordesired for given application.

Herein provided are illustrative embodiments to demonstrate specificexamples of the present invention method and system, or components ofthe system. These exemplary embodiments of the system or of theindividual components should be considered illustrative only rather thanrestrictive.

Example No. 1

FIGS. 3(A)-(B) are schematic plan and elevation views, respectively, ofan embodiment of the present invention acceleration, vibration, and/ordeflection module 32. Provided is an embodiment of the present inventionacceleration, vibration, and/or deflection module 32 that detects flooracceleration, vibration, and/or deflection to provide acceleration,vibration, and/or deflection signal. The gait sensor 32 transduces thedisplacement of the floor surface 95 into a waveform signal which can beretained and analyzed. The design employs a linear variable differentialtransformer (LVDT) 92. The LVDT 92 may also be any low mass, highresolution displacement sensor technology including but are not limitedto magnetic coil induction; laser light reflection; changes in Plasmonsurface resonance; RF; changes in light due to luminescence; dopplerradar; and/or any sensor technology that transduces the minutedeflections of the floor induced by gait or falling or dropping ofobjects. The stationary part of the assembly is suspended by a vibrationdamping material 96 or structure. The resulting sensor assembly issuspended from a significant mass 98, where that mass is supported byadditional damping material 97. The significant mass 98 may also be anonmagnetic material, for example brass, to add mass to the system orany suitable material. The damping material 97 may also be any suitablevibration damping material or structure, for example foam or the like.The exciter rod 94 of the LVDT extends to the floor surface 95. Wheneverthere is a displacement of the floor, the moment of inertia of thesystem is greater than the exciter rod 94, allowing the rod to move inrelation to the greater mass, thus deriving a signal from a sensor thatdoes not rely on a different plane (wall) to obtain reference. Theadjustable exciter rod 94 is configured to transfer displacement offloor to active element of the sensor.

FIGS. 3(C)-(D)) show a graphical representation of the “raw” and postprocessing signals produced in the embodiment of FIGS. 3(A)-(B), whereinthe steps are as captured in FIG. 3(1)) while the raw signal shows therich harmonic content as captured in FIG. 3(C).

Example No. 2

Referring to FIG. 4, FIG. 4 illustrates a schematic a block diagram ofan example of an embodiment of the present invention system 61. In thecurrent embodiment, the fall detection is a separate entity from thestep counter 78, which allows the calculation of cadence, and from thelimp detector 74 and shuffle detector 72. The fall detector consists ofa second order Butterworth band-pass filter (about 30 Hz-50 Hz) 62. Thisfilter was tuned to block frequencies generated by different walk modesor dropped objects and to yield the highest output in response tofalling people. However, it should be appreciated that other filterdesigns, including filter type, order and frequencies, may yield similarresults. The filter output feeds into an amplifier and comparator 70 todetect falls; the comparator threshold is tuned to detect a low weighthuman falling about ten feet (or as desired) from the sensor, yet remaininsensitive to dropped objects. This reduces potential false alarmswithout compromising sensitivity to human fall detection.

For detecting step timing and determining limp and, shuffle, theoriginal signal is filtered through a second order Butterworth low-passfilter 64 having a cutoff frequency of 3 Hz. However, experts in the artunderstand that other filter designs, including filter type, order andfrequencies, may yield similar results. The filtered signal is processedto produce a large signal that corresponds with footfalls of a walkingperson. This processing involves taking the derivative of the filteredsignal followed by a second stage of low-pass filtering at 10 Hz toremove noise. Similar results can be obtained using a high-gainhigh-pass filter to approximate the derivative. The processed signal ispassed through a peak detector 68 to detect negative peaks in thesignal, which correspond with footfalls. Counting the peaks provides astep count and allows the calculation of cadence, defined as the numberof steps taken per minute. Step timing information can also be derivedby running the processed signal through a zero crossing comparator.Algorithms to compare peak amplitudes, step counts and step timing thatprovides an accurate estimate of whether a person is limping, shufflingor walking normally.

The above-described system design, including the circuits and theassociated algorithms, was simulated on Matlab and Simulink. Real rawsensor data, recorded from a set of experiments carried out on carpetedand uncarpeted wooden floors with a person walking towards and away fromthe sensor, was input into the simulation model. The results showsuitability for a range of different applications.

Example No. 3

FIG. 5 shows a graphical representation of an example of the resultsobtained applying an exemplary circuit model to the data obtained fromthe floor vibration sensor for a normal walk. FIG. 6 shows a graphicalrepresentation of an example of the results obtained applying anexemplary circuit model to the data obtained from the floor vibrationsensor for a limp. FIG. 7 shows a graphical representation of an exampleof the results obtained applying an exemplary circuit model to the dataobtained from the floor vibration sensor for a shuffle. FIG. 8 shows agraphical representation of an example of the results obtained applyingan exemplary circuit model to the data obtained from the floor vibrationsensor for a fall.

Turning to FIG. 5, shows FIG. 5 the graphically shows the signalsgenerated by a person walking in a normal gait mode, together with thederived signals throughout the various processing stages in our circuitmodel. Knowing the location of the sensor during the experiments, it wasnoted that the amplitude of the detected peaks, corresponding tofootfalls, consistently increased when the person walks towards thesensor, and consistently decreased when the person walks away. One cancount the steps, by counting the detected peaks, and then calculatecadence based on the time period during which the steps were taken. Inthis particular experiment, the number of actual steps taken was 13 andour system detected and counted 14 peaks. The difference in the count isa result of falsely counting the first negative peak as a step; thispeak is an artifact resulting from filtering data collected off-line.This artifact can be eliminated through the use of a hardware prototype,implementing the circuit design, to filter and process vibration signalsin real time. Thus the graph shows that the timing of the steps wasfairly regular in normal gait modes.

Turning to FIG. 6, FIG. 6 the graphically shows signals generated fromdata of a limping person. An alternating pattern for the amplitude ofthe peaks, high-low-high or low-high-low, was observed; this alternatingpattern reflects the difference in percussive pressure applied to thefloor by both feet. The amplitude of the peaks still shows an overalltrend of increase with the person walking towards the sensor anddecrease when walking away from it. However, the difference inalternating amplitudes is higher than the increase or decrease due toproximity to the sensor. Moreover, the alternation pattern is reversedwhen the person approaches the sensor and walks away. The peaks countedin this case were 16 whereas the actual number of steps taken was only14. The difference here may also be attributed to the artifactsresulting from filtering data off-line. From the figure, one can observeirregular and skewed step timing in case of limping.

Turning to FIG. 7, FIG. 7 the graphically shows the signal generated bya shuffling subject and the derivations of the signal. From shufflingdata, one can observe a large number of low amplitude peaks that have nocorrelation with each other or proximity to the sensor. In case ofshuffling, irregular and skewed step timing signal is also noticed.

Turning to FIG. 8, the methodology included performing extensiveexperiments with falling objects on both carpeted and uncarpeted woodenfloors and a few experiments with falling people only on carpetedfloors. Extensive simulations and design iterations allowed the examplemethodology to successfully differentiate falling objects from fallingpeople through tuning the band pass filter employed to a specificfrequency range to increase sensitivity to falling people and to reducethe probability of false alarms resulting from falling objects. Inparticular, FIG. 8 shows the output of the fall detector successfullytriggered by a person, weighing 175 lb., falling 9 feet away from thesensor on a carpeted area of a wooden floor.

Turning to FIG. 9, shows FIG. 9 the graphically shows an exampledemonstrating the fall detector's reduced sensitivity to fallingobjects. In this experiment an object, weighing 3 lb., was dropped froma height of 1 ft. onto the uncarpeted section of the wooden floor 7.3ft. away from the sensor.

Next, the method of present invention may be implemented using hardware,software or a combination thereof and may be implemented in one (or partof) or more computer systems or other processing systems, such aspersonal digit assistants (PDAs) or in communication with the same.

In an example embodiment, the invention was implemented in softwarerunning on a general purpose computer or the like. Computer systemincludes one or more processors. Such processor may be connected to acommunication infrastructure (e.g., a communications bus, cross-overbar, or network). The computer system may include a display interfacethat forwards graphics, text, and other data from the communicationinfrastructure (or from a frame buffer not shown) for display on thedisplay unit.

The Computer system may also include a main memory, preferably randomaccess memory (RAM), and may also include a secondary memory. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, an optical disk drive, a flash memory etc. The removablestorage drive reads from and/or writes to a removable storage unit in awell known manner. Removable storage unit, represents a floppy disk,magnetic tape, optical disk, etc. which is read by and written to byremovable storage drive. As will be appreciated, the removable storageunit includes a computer usable storage medium having stored thereincomputer software and/or data.

In alternative embodiments, secondary memory may include other means forallowing computer programs or other instructions to be loaded intocomputer system. Such means may include, for example, a removablestorage unit and an interface. Examples of such removable storageunits/interfaces include a program cartridge and cartridge interface(such as that found in video game devices), a removable memory chip(such as a ROM, PROM, EPROM or EEPROM) and associated socket, and otherremovable storage units and interfaces which allow software and data tobe transferred from the removable storage unit to computer system.

The computer system may also include a communications interface.Communications interface allows software and data to be transferredbetween computer system and external devices. Examples of communicationsinterface may include a modem, a network interface (such as an Ethernetcard), a serial or parallel communications port, a PCMCIA slot and card,a modem etc. Software and data transferred via communications interfaceare in the form of signals, which may be electronic, electromagnetic,optical or other signals capable of being received by communicationsinterface. Signals are provided to communications interface 124 via acommunications path (i.e., channel). A channel (or any othercommunication means or channel disclosed herein) carries signals and maybe implemented using wire or cable, fiber optics, a phone line, acellular phone link, an RF link, an infrared link and othercommunications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage drive, a hard disk installed in hard disk drive, and signals.These computer program products are means for providing software tocomputer system. The invention includes such computer program products.

Computer programs (also called computer control logic) are stored inmain memory and/or secondary memory. Computer programs may also bereceived via communications interface. Such computer programs, whenexecuted, enable computer system to perform the features of the presentinvention as discussed herein. In particular, the computer programs,when executed, enable processor to perform the functions of the presentinvention. Accordingly, such computer programs represent controllers ofcomputer system.

In an embodiment where the invention is implemented using software, thesoftware may be stored in a computer program product and loaded intocomputer system using removable storage drive, hard drive orcommunications interface. The control logic (software), when executed bythe processor, causes the processor to perform the functions of theinvention as described herein.

In another embodiment, the invention is implemented primarily inhardware using, for example, hardware components such as applicationspecific integrated circuits (ASICs). Implementation of the hardwarestate machine to perform the functions described herein will be apparentto persons skilled in the relevant art(s).

In yet another embodiment, the invention is implemented using acombination of both hardware and software.

In an example software embodiment of the invention, the methodsdescribed above may be implemented in various programs and programminglanguage known to those skilled in the art.

The following publications, patent applications, and patents are herebyincorporated by reference herein in their entirety:

U.S. Pat. No. 6,360,597 B1 to Hubbard, Jr., entitled “In-Shoe RemoteTelemetry Gait Analysis System;”

U.S. Pat. No. 6,301,964 B1 to Fyfe et al., entitled “Motion AnalysisSystem;”

U.S. Pat. No. 6,168,569 B1 to McEwen et al., entitled “Apparatus andMethod for Relating Pain and Activity of a Patient;”

U.S. Pat. No. 6,145,389 to Ebeling et al., entitled “Pedometer Effectivefor both id Walking and Running;”

U.S. Pat. No. 6,095,991 to Krausman et al., entitled “Ambulatory BodyPosition Monitor;”

U.S. Pat. No. 6,010,465 to Nashner, entitled “Apparatus and Method forCharacterizing Gait;”

U.S. Pat. No. 5,952,585 to Trantzas et al., entitled “Portable PressureSensing Apparatus for Measuring Dynamic Gait Analysis and Method ofManufacture;”

U.S. Pat. No. 5,919,149 to Allum, entitled “Method and Apparatus forAngular Position and Velocity Based Determination of Body Sway for theDiagnosis and Rehabilitation of Balance and Gait Disorders;”

U.S. Pat. No. 5,831,937 to Weir et al., entitled “Portable RangingSystem for Analyzing Gait;”

U.S. Pat. No. 5,807,283 to Ng, entitled “Activity Monitor;”

U.S. Pat. No. 5,623,944 to Nashner, entitled “Method for CharacterizingGait;”

U.S. Pat. No. 5,511,571 to Adrezin et al., entitled “Method andApparatus for Gait Measurement;”

U.S. Pat. No. 5,511,561 to Wanderman et al., entitled “Gait Cycle ForceMonitor;”

U.S. Pat. No. 5,485,402 to Smith et al., entitled “Gait ActivityMonitor;”

U.S. Pat. No. 5,474,087 to Nashner, entitled “Apparatus forCharacterizing Gait;”

U.S. Pat. No. 5,337,757 to Jain et al., entitled “Device for Inducingand Registering Imbalance;”

U.S. Pat. No. 5,186,062 to Roost, entitled “Method of Investigating theGait of a Living Being;”

U.S. Pat. No. 5,138,550 to Abraham et al., entitled “Device forMonitoring the Gait in Particular of a Horse and Monitoring System toWhich it is Applied;”

U.S. Pat. No. 4,813,436 to Au, entitled “Motion Analysis SystemEmploying Various Operating Modes;”

U.S. Pat. No. 4,651,446 to Yukawa et al., entitled “ElectronicPedometer;”

U.S. Pat. No. 4,600,016 to Boyd et al., entitled “Method and Apparatusfor Gait Recording and Analysis;”

U.S. Pat. No. 4,510,704 to Johnson, entitled “Boot or Shoe IncorporatingPedometer or the Like;”

U.S. Pat. No. 4,387,437 to Lowery et al., entitled “Runners Watch;”

U.S. Pat. No. 4,371,945 to Karr et al., entitled “Electronic Pedometer;”

U.S. Pat. No. 4,223,211 to Allsen et al., entitled “Pedometer Devices;”

U.S. Pat. No. 4,192,000 to Lipsey, entitled “Electronic CalorieCounter;”

U.S. Pat. No. 4,144,568 to Hiller et al., entitled “Exercise Recorder;”

U.S. Pat. No. 6,696,956 B1 to Uchida et al., entitled “EmergencyDispatching System;”

U.S. Pat. No. 6,687,424 B1 to Gerdt et al., entitled “Sensing PadAssembly Employing Variable Coupler Fiberoptic Sensor;”

U.S. Pat. No. 6,659,968 B1 to McClure, entitled “Activity Monitor forPain Management Efficacy Measurement;”

U.S. Pat. No. 6,640,212 B1 to Rosse, entitled “Standardized InformationManagement System for Long-Term Residence Facilities;”

U.S. Pat. No. 6,571,193 B1 to Unuma et al., entitled “Method, Apparatusand System for Recognizing Actions;”

U.S. Pat. No. 6,524,239 B1 to Reed et al., entitled “Apparatus forNon-Intrusively Measuring Health Parameters of a Subject and Method ofUse Thereof;”

U.S. Pat. No. 6,515,586 B1 to Wymore, entitled “Tactile Tracking Systemsand Methods;”

U.S. Pat. No. 6,463,187 B1 to Baruch et al., entitled “Variable CouplerFiberoptic Sensor and Sensing Apparatus Using the Sensor;”

U.S. Pat. No. 6,221,010 B1 to Lucas, entitled “Home Medical Supervisionand Monitoring System;”

U.S. Pat. No. 6,221,010 B1 (Certificate of Correction) to Lucas,entitled “Home Medical Supervision and Monitoring System;”

U.S. Patent Application Publication 2003/0199771 A1 to Baruch et al.,entitled “Apparatus and Method for Measuring Pulse Transit Time;” and

U.S. Patent Application Publication 2002/0107649 A1 to Takiguchi et al.,entitled “Gait Detection System, Gait Detection Apparatus, Device, andGait Detection Method.”

In summary, the present invention gait monitor system and methoddescribed herein can provide various basic gait parameters includingstep count, cadence, and step duration, in addition to its ability todistinguish between normal, limping and shuffling gait modes as well asbut not limited to physical forces that are currently known in gait suchas heel impact, roll from heel to toe, toe-off, heel rotation, forcesgenerated when the toes push off the floor to get locomotion (e.g.gastricnemeous contraction), knee flexure, hip rotation, and pelvisswivel, etc. These individual force generators should also be integratedinto the amount of forward motion that is generated, versus the amountof motion that is spent lifting the body. However, this gait monitor maybe augmented with additional sensors, e.g. beam break at the beginningand end of a corridor to estimate average walking velocity (with thedistance between the beams known or determined); this enables thecalculation of additional gait characteristics such as average steplength and average stride length. These parameters can additionally beused to detect various gait anomalies.

Still other embodiments will become readily apparent to those skilled inthis art from reading the above-recited detailed description anddrawings of certain exemplary embodiments. It should be understood thatnumerous variations, modifications, and additional embodiments arepossible, and accordingly, all such variations, modifications, andembodiments are to be regarded as being within the spirit and scope ofthe appended claims. For example, regardless of the content of anyportion (e.g., title, section, abstract, drawing figure, etc.) of thisapplication, unless clearly specified to the contrary, there is norequirement for any particular described or illustrated activity orelement, any particular sequence of such activities, any particularsize, speed, dimension or frequency, or any particular interrelationshipof such elements. Moreover, any activity can be repeated, any activitycan be performed by multiple entities, and/or any element can beduplicated. Further, any activity or element can be excluded, thesequence of activities can vary, and/or the interrelationship ofelements can vary. Accordingly, the descriptions and drawings are to beregarded as illustrative in nature, and not as restrictive.

1. A gait monitoring system for monitoring gait characteristics of asubject, said system comprising: a sensor module configured to touch anupper surface of a floor, the sensor module configured to while touchingthe upper surface of the floor detect at least one of flooracceleration, floor vibration, and floor deflection and to provide atleast one of an acceleration, vibration, and deflection signal, whereinthe subject can walk on the upper surface of the floor in proximity tosaid sensor module; and a processor that is configured to analyze theacceleration, vibration, and deflection signal and to determine gaitcharacteristics based on the signal, wherein, the processor isconfigured to determine the gait characteristics based on the at leastone signal from a single sensor module; and the processor is configuredto detect a fall of a human being by distinguishing between steps of thehuman being and the fall of the human being and comparing the data to athreshold.
 2. The system of claim 1, further comprising: an outputmodule for receiving data indicative of the gait characteristics.
 3. Thesystem of claim 2, wherein said output module comprises at least one ofdisplay, alarm, memory storage, communication device, printer, buzzer,PDA, lap top computer, computer, audio or visual alarm, and light. 4.The system of claim 3, wherein said communication device comprises atleast one of modem, pager, network interface, Ethernet card, serialcommunications port, parallel communications port, telephone, and PCMCIAslot and card.
 5. The system of claim 1, wherein said sensor module andprocessor module are in wireless communication.
 6. The system of claim5, wherein said wireless communication comprises at least one of RFlink, an infrared, cellular phone link, optical and electromagnetic. 7.The system of claim 1, wherein said sensor module and processor moduleare in a hard wired communication.
 8. The system of claim 7, whereinsaid hard wired communication comprises at least one of electronic,integrated circuit, electromagnetic, wire, cable, fiber optics, a phoneline, twisted pair, and coaxial.
 9. The system of claim 1, furthercomprising: a rate-of-travel detector to determine the rate-of-travel ofthe subject.
 10. The system of claim 9, wherein said rate-of-traveldetector comprises at least one of a plurality of beam breaks, floorswitches, and door switches.
 11. The system of claim 9, wherein saidrate-of-travel detector comprises at least one of ultrasoniccommunication, IR communication, laser communication, ground radarcommunication, wide band radar communication, and doppler communication.12. The system of claim 1, wherein said gait characteristics of thesubject includes at least two of step count, pace, normal gaitcondition, limp, shuffle, and falls.
 13. The system of claim 1, furthercomprising an archival storage module.
 14. The system of claim 13,wherein the processor module is configured to perform at least two oflongitudinal analysis of gait characteristics, pattern recognition, andidentification determination, wherein identification determinationassociates gait characteristics with a particular subject and saidarchival storage module stores the at least two of longitudinal analysisof gait characteristics, pattern recognition, and identificationdetermination.
 15. The system of claim 1, further comprising: a secondprocessor module, wherein said second processor module is configured toanalyze gait characteristics, pattern recognition, and identificationdetermination data, the identification determination data associatinggait characteristics with a particular subject.
 16. The system of claim1, wherein the subject is one of a human and an animal.
 17. The systemof claim 1, further comprising a fall module configured to: providenotification of a fall based on the data.
 18. The system of claim 1,further comprising a step module configured to process data receivedfrom said sensor module.
 19. The system of claim 1, further comprising:a second processor module in communication with said system.
 20. Amethod for monitoring gait characteristics of a subject, said methodcomprising: detecting at least one of a floor acceleration, a floorvibration, and a floor deflection to provide at least one of anacceleration, vibration, and deflection signal, wherein said detectingis provided by a sensor module touching an upper surface of a floor, andwherein the subject walks on the upper surface of the floor in proximityto said sensor module; using a processor analyzing the at least onesignal; and determining gait characteristics based on the at least onesignal, wherein, the gait characteristics are determined based on the atleast one signal from a single sensor module; and the analyzing furtherincludes detecting a fall of a human being by distinguishing betweensteps of the human being and the fall of the human being and comparingthe data to a threshold.
 21. The method of claim 20, further comprising:outputting data indicative of the gait characteristics.
 22. The methodof claim 21, wherein said outputting is provided by an output modulethat comprises at least one of display, alarm, memory storage,communication device, printer, buzzer, PDA, lap top computer, computer,audio or visual alarm, and light.
 23. The method of claim 22, whereinsaid communication device comprises at least one of modem, pager,network interface, Ethernet card, serial communications port, parallelcommunications port, telephone, and PCMCIA slot and card.
 24. The methodof claim 20, further comprising: detecting rate-of-travel of the subjectto determine the rate-of-travel of the subject.
 25. The method of claim24, wherein said detecting the rate-of-travel is provided by arate-of-travel detector.
 26. The method of claim 24, wherein saiddetecting the rate-of-travel comprises at least one of ultrasoniccommunication, IR communication, laser communication, ground radarcommunication, wide band radar communication, and doppler communication.27. The method of claim 20, wherein the gait characteristics of thesubject includes at least two of step count, pace, normal gaitcondition, limp, shuffle, and falls.
 28. The method of claim 20, furthercomprising: storing archival information or data.
 29. The method ofclaim 28, wherein the storing of archival information or data isprovided by an archival storage module that stores at least two oflongitudinal analysis of gait characteristics, pattern recognition, andidentification determination, the identification determinationassociating gait characteristics with a particular subject.
 30. Themethod of claim 29, further comprising: analyzing the gaitcharacteristics, pattern recognition, and identification determinationdata.
 31. The method of claim 20, wherein the subject is one of a humanand animal.
 32. The method of claim 20, further comprising:automatically identifying signals indicative of a human body falling todetermine fall data.
 33. The method of claim 20, further comprising:analyzing step data from the at least one signal.
 34. The method ofclaim 20, wherein the distinguishing between steps of a human being anda fall of a human being is based on filtering out a frequencycorresponding to at least one of walking modes and dropped objects. 35.A computer program product comprising non-transitory computer usablemedium having computer logic embedded thereon for enabling at least oneprocessor in a computer system to monitor gait characteristics of asubject, said computer logic configured to cause the computer system to:receive at least one of a floor acceleration, a floor vibration, and afloor deflection signal, wherein said at least one signal is provided bya sensor module touching an upper surface of a floor, and wherein thesubject can walk on the upper surface of the floor in proximity to saidsensor module; and analyze the at least one signal; and determine gaitcharacteristics based on the at least one signal, wherein, the gaitcharacteristics are determined based on the at least one signal from asingle sensor module; and the analyzing further including detecting afall of a human being by distinguishing between steps of the human beingand the fall of the human being and comparing the data to a threshold.36. The computer program product of claim 35, wherein the distinguishingbetween steps of a human being and a fall of a human being is based onfiltering out a frequency corresponding to at least one of walking modesand dropped objects.
 37. A gait monitoring system, said systemcomprising: a sensor device comprising: a housing configured to beplaced on a floor surface in a freestanding position; and a sensorconfigured to: touch the floor surface; detect while touching the floorsurface at least one of floor acceleration, floor vibration, and floordeflection; and generate a signal based on the detected at least one offloor acceleration, floor vibration, and floor deflection; a processorunit configured to communicate with the sensor device and determine gaitcharacteristics based on the signal, the determining gaitcharacteristics including identifying at least two of a normal gaitcharacteristic, limping, shuffling, and falling of a human being bycomparing the signal to respective characteristic patterns; and anoutput device configured to output the determined gait characteristics,the output including different outputs for the at least two of normalgait characteristic, abnormal gait characteristic and human body fall.38. The system of claim 37, wherein the processor unit is configured todetermine the gait characteristics based on the signal from a singlesensor device.
 39. The system of claim 37, wherein the processor unit isconfigured to identify the human body fall based on filtering out afrequency corresponding to at least one of walking modes and droppedobjects.